AI Transformation Leader

Associate Distinguished Engineer AI, Data Science & Agentic Solutions

As an Associate Distinguished Engineer AI, Data Science & Agentic Solutions, you will act as a senior technical authority responsible for architecting, validating, and scaling next-generation AI systems across enterprises. This role is deeply hands-on with modern AI/ML ecosystems, agentic architectures, large-scale data platforms, and cloud-native engineering patterns.

You will partner with senior technology leaders to define end-to-end AI architectures, recommend engineering strategies, and shape the technical roadmap for deploying advanced AI systems such as LLMs, multimodal models, agentic pipelines, retrieval systems, and enterprise ML/LLMOps platforms.

Your focus is to guide solution direction, validate architectural decisions, and remove technical ambiguity, ensuring organizations adopt AI responsibly, securely, and at scale while staying independent of daily execution cycles.

Key Responsibilities

1. AI Architecture & Technical Leadership

• Architect enterprise-grade AI systems using LLMs, multimodal models, vector databases, knowledge graphs, and agentic orchestration frameworks.
• Design end-to-end pipelines including data ingestion feature engineering model training evaluation deployment feedback loops.
• Define and enforce engineering standards for MLOps, LLMOps, data quality, model observability, guardrails, prompt security, and hallucination mitigation.
• Consult on scalable microservices, model serving layers, retrieval-augmented generation (RAG) pipelines, and autonomous agent workflows.
• Conduct architectural reviews, performance tuning, and technical due-diligence for high-risk or complex AI solutions.

1. Advanced AI/ML Engineering

• Guide on how to build quick prototypes, PoCs, and production systems using modern AI stacks (transformer models, diffusion models, graph models, reinforcement learning, and agentic systems).
• Advise on selection of foundation models and fine tuning approaches.
• Advise on real-time data streams, event-driven systems, API layers, and cloud-native compute.
• Establish evaluation frameworks: bias, drift, explainability, reliability, performance.
• Lead complex troubleshooting, debugging, and optimization of AI pipelines and distributed training workloads.

1. Data Platform & Infrastructure Architecture

• Architect secure, high-throughput data platforms for AI/BI use cases based on lakehouse, medallion, streaming, and vectorized storage patterns.
• Define data governance, metadata, lineage, cataloging, and policy enforcement mechanisms.
• Deploy scalable compute using Databricks, Snowflake, Kubernetes, Ray, SageMaker, Vertex AI, and Azure ML.

1. Technical Advisory & Engineering Governance

• Guide CIO/CTO/CDO teams on AI system design, architecture modernization, model lifecycle governance, and platform engineering standards.
• Translate ambiguous requirements into well-scoped technical blueprints, reference architectures, and engineering backlogs.
• Evaluate enterprise readiness across data, models, infrastructure, and processes producing AI maturity assessments and architectural recommendations.
• Mentor engineering teams in building reliable, secure, and scalable AI systems with measurable outcomes.

1. Innovation & Ecosystem Leadership

• Lead deep-dive technical workshops on agentic systems, generative AI patterns, model safety architectures, continuous learning loops, and intelligent automation.
• Collaborate with hyperscalers and partners (AWS, Azure, Google Cloud Platform, Databricks, Snowflake, NVIDIA) on technical accelerators, performance benchmarks, and reference implementations.
• Stay ahead of emerging architectures (multi-agent, RAG 2.0, synthetic data generation, self-improving systems) and translate them into actionable engineering strategies.

Qualifications

• 12 years in AI/ML, data engineering, or large-scale distributed systems.
• Deep hands-on expertise in:
• Foundation models (LLMs, multimodal, vision, speech, embeddings)
• Model finetuning, training, inference optimization, evaluation
• MLOps/LLMOps workflows and ML engineering best practices
• Vector databases, knowledge graphs, retrieval systems
• Strong experience with cloud-native architectures (AWS, Azure, Google Cloud Platform) and data platforms (Databricks, Snowflake, BigQuery, Lakehouse).
• Demonstrated ability to design complex AI systems that operate reliably at scale.
• Experience influencing senior technology leaders through architectural clarity and technical depth.
• Strong documentation, architecture storytelling, and ability to simplify complex technical concepts for varied audiences.
• Track record of publications, open-source contributions, patents, technical talks, or recognized technical leadership is a strong plus.